Perfusion analysis is used, for example in medical imaging, for a number of different purposes. For example, differential diagnosis between benign and malignant pulmonary lesions such as lung nodules may be performed using perfusion imaging and analysis. Another example is differentiation of positive or negative response of tumors to cancer therapy. Use may be made of dynamic CT scans. Such a dynamic CT scan comprises a time series of two-dimensional or three-dimensional scans including scans taken before and after administering a contrast agent, such as iodine. The scans result in images comprising image elements such as voxels (in the case of three-dimensional images) or pixels (in the case of two-dimensional images). The ‘uptake’ or ‘enhancement’ which may be visible in the images and which is due to the contrast agent's arrival at the region of interest, for example the lesion or tumor, may be interpreted as a surrogate for angiogenesis. Such angiogenesis may be a sign of malignancy and/or metabolic activity.
The uptake or enhancement is determined using an image element-by-image element (for example, voxel-wise or pixel-wise) comparison of intensity values in successive images in an image sequence, because at the time the contrast agent arrives in a region of interest, the intensity values of voxels (or pixels) in the region of interest will change. However, the uptake or enhancement is often small in comparison to the image contrast. For example, the intensity-value change caused by the contrast agent may be only 1%. Consequently, it is difficult to accurately determine the timing and/or strength of the uptake or enhancement. Moreover, the images or image volumes of a time series may not be perfectly aligned spatially. For example, the patient may have moved or changed position or an internal organ may have been deformed due to for example breathing, heartbeat, or bowel movement.
“Texture analysis of lesion perfusion volumes in dynamic contrast-enhanced breast MRI”, by Sang Ho Lee et al., in: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI, 2008, pages 1545-1548, hereinafter: Lee et al., discloses a texture analysis scheme applied to perfusion volumes in dynamic contrast-enhanced breast MRI to provide a method of lesion discrimination. Automatic segmentation was performed for extraction of a lesion volume, which was divided into whole, rim, and core volume partitions. Lesion perfusion volumes were classified using a three-time-points (3TP) method of computer-aided diagnosis. According to the known 3TP method, three selected time points along the uptake characteristic are used to estimate washin and washout behavior.